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A house price index defined in the potential outcomes framework

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  • Nicholas Longford

Abstract

Current methods for constructing house price indices are based on comparisons of sale prices of residential properties sold two or more times and on regression of the sale prices on the attributes of the properties and of their locations. The two methods have well recognised deficiencies, selection bias and model assumptions, respectively. We introduce a new method based on propensity score matching. The average house prices for two periods are compared by selecting pairs of properties, one sold in each period, that are as similar on a set of available attributes (covariates) as is feasible to arrange. The uncertainty associated with such matching is addressed by multiple imputation, framing the problem as involving missing values. The method is applied to aregister of transactions ofresidential properties in New Zealand and compared with the established alternatives.

Suggested Citation

  • Nicholas Longford, 2009. "A house price index defined in the potential outcomes framework," Economics Working Papers 1175, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:1175
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    References listed on IDEAS

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    1. Karl E. Case & Robert J. Shiller, 1987. "Prices of single-family homes since 1970: new indexes for four cities," New England Economic Review, Federal Reserve Bank of Boston, issue Sep, pages 45-56.
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    3. Gatzlaff, Dean H & Haurin, Donald R, 1997. "Sample Selection Bias and Repeat-Sales Index Estimates," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 33-50, Jan.-Marc.
    4. Rosenbaum, Paul R. & Ross, Richard N. & Silber, Jeffrey H., 2007. "Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 75-83, March.
    5. Goodman, Allen C., 1978. "Hedonic prices, price indices and housing markets," Journal of Urban Economics, Elsevier, vol. 5(4), pages 471-484, October.
    6. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    7. Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
    8. Kiel, Katherine A & Zabel, Jeffrey E, 1997. "Evaluating the Usefulness of the American Housing Survey for Creating House Price Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 189-202, Jan.-Marc.
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    Cited by:

    1. Lopez, Esteban & Hewings, Geoffrey J.D., 2018. "Housing price indices for small spatial units," Regional Science and Urban Economics, Elsevier, vol. 70(C), pages 57-71.
    2. N. T. Longford & Pierpaolo D'Urso, 2011. "Mixture models with an improper component," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2511-2521, January.
    3. Nik Mohd Sukrri, Nik Nor Amalina & Abd. Wahab, Norazlina & Mohd. Yusof, Rosylin, 2019. "Constructing an Enhanced House Price Index Model: Empirical Evidence," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 53(3), pages 117-128.

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    More about this item

    Keywords

    Hedonic regression; house prices; matching; potential outcomes; propensity scoring; repeat-sales method;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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